High capacity models learn human-human and human-space interactions in a more generic,
data-driven fashion. They are data hungry with respect to hand-crafted methods.
Yet, with enough data, they outperform previous works in terms of prediction
accuracy. Since 2016, a collection of methods have been proposed based on
Recurrent Neural Networks (RNN) [1]}, [2]}, [3]}.
The challenge remains on how to design the RNN architectures, or how to
represent interactions. Alahi et al. [2]} suggest a
social tensor to represent human-human interactions.
Gupta et al. [3]} present an adversarial training to learn
the distribution of trajectories. While these methods have the capacity to
learn complex interactions, it is difficult to understand the “why” or simply
the impact of surrounding humans on each other. Hence, we propose to take the
benefits of both streams of methods and introduce a non-parametric, Deep Social Force model.
